Handling Uncertainty in Accessing Petroleum Exploration Data

Chung, Pei-Jung; Inder, Robert

doi:10.2516/ogst:1992021

Cited by 7 publications

(2 citation statements)

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“…Fuzzy techniques also work in reverse, allowing matching of precise values against a fuzzy set of requirements, or range of data, provided by the user of the system. An example of an application which uses fuzzy techniques can be found in Chung and Inder (1991), where techniques were developed to access geological databases containing fuzzy values to support the task of petroleum exploration.…”

Section: / Fuzzy Knowledge or Datamentioning

confidence: 99%

Artificial intelligence in business II: Development, integration and organizational issues

O’Leary

Kingston

1994

The Knowledge Engineering Review

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The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper provided a broad survey of the use of AI in business, summarizing the application of AI in a number of business domains. In addition, it also provided a summary of the use of different forms of knowledge representation in business applications. Part I has a large set of references, including a number of survey papers, focusing on AI in business. Part II of this paper consists of more detailed analysis of particular systems or issues affecting AI in business. It examines technical issues which are central to the construction of business AI systems, and it also examines the commercial contribution made by methods for the development of AI systems. In addition, part II looks at integration between AI and more traditional information systems. AI can be used to add value to many existing information systems, such as database management systems. Particular attention is given to the integration of AI with operations research, which is the one of the primary “competitors” of AI, providing an alternative set of support tools for decision making.Business organizations are not concerned only with technology issues; there is also concern about the impact of AI on organizations. Further, the evaluation of AI often is based on an economic view of the world. Part II therefore investigates the organizational impact of AI, and the economics of AI, including issues such as value creation. The format of Part II is as follows: Section 8 analyses techniques for improving the performance of AI systems, thus maximizing economic return. Section 9 looks at different forms of uncertainty and ambiguity which must be dealt with by AI systems. It examines the contributions of fuzzy logic and numerical measures of certainty to handling these problems. Section 10 examines the usefulness of different approaches to knowledge acquisition in business situations, and investigates the benefits of methodological approaches to AI applications. It also looks at more recent AI programming techniques which eliminate the need for knowledge elicitation from an expert: neural networks, case-based reasoning and genetic algorithms are discussed. Sections 11 and 12 examine issues of integrating AI systems. Generally, the use of AI in business settings must ultimately be integrated with the broader base of corporate information systems. Section 11 looks at integration with information systems in general, and section 12 looks particularly at integration with operations research. Sections 13 and 14 review the organizational and economic impact of AI. Finally, section 15 provides a brief summary of part II.

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Section: / Fuzzy Knowledge or Datamentioning

confidence: 99%

Artificial intelligence in business II: Development, integration and organizational issues

O’Leary

Kingston

1994

The Knowledge Engineering Review

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“…By using fuzzy set theory, the width to depth ratio and the degree of an estuary's sinuosity are expressed according to a qualitative scale. In addition, some indices such as the geomorphological type, the tidal range and the salinity stratification, as object symbols(Chung & Inder 1992) are defined according to the description provided by the estuaries' classification schemes.…”

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A hybrid reasoning system for supporting estuary modelling

Passone

Nassehi

Chung

2005

Journal of Hydroinformatics

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In this paper the development of a Case-Based reasoning system for Estuarine Modelling (CBEM) is presented. The aim of the constructed CBEM system is to facilitate the utilisation of complex modelling software by users who lack detailed knowledge about modelling techniques and require training and assistance to implement sophisticated software effectively. The system is based on modern computing methods and is constructed as a hybrid of three modules which operate conjunctively to guide the user to obtain the best possible simulation for realistic problems. These modules are: a case-based reasoning scheme, a genetic algorithm and a library of numerical estuarine models. Based on the features of a given estuary and the physical phenomenon to be modelled, an appropriate solution algorithm from the system's library is retrieved by the case-based module after a specifically designed reasoning process. The selected model is then analysed and further treated by the genetic algorithm component to find the optimum parameters which can appropriately model the conditions and characteristics of any given estuary. Using these modules the steps that yield the best solution for a problem from the available hydrographic data under a set of specified conditions are explained. This is further elucidated by an illustrative case study which shows the applicability of the present CBEM system under realistic conditions. This case deals with the simulation of salinity distribution in the Tay estuary (Scotland, UK).

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Bags and Viewers: A Metaphor for Intelligent Database Access

Inder

Stader

1995

Workshops in Computing

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Handling Uncertainty in Accessing Petroleum Exploration Data

Cited by 7 publications

References 0 publications

Artificial intelligence in business II: Development, integration and organizational issues

Artificial intelligence in business II: Development, integration and organizational issues

A hybrid reasoning system for supporting estuary modelling

Bags and Viewers: A Metaphor for Intelligent Database Access

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